Measuring Tomorrow: Accounting for Well-Being, Resilience, and Sustainability in the Twenty-First Century

Curated on Posted on by Stefaan Verhulst

Book by Éloi Laurent on “How moving beyond GDP will improve well-being and sustainability…Never before in human history have we produced so much data, and this empirical revolution has shaped economic research and policy profoundly. But are we measuring, and thus managing, the right things—those that will help us solve the real social, economic, political, and environmental challenges of the twenty-first century? In Measuring Tomorrow, Éloi Laurent argues that we need to move away from narrowly useful metrics such as gross domestic product and instead use broader ones that aim at well-being, resilience, and sustainability. By doing so, countries will be able to shift their focus away from infinite and unrealistic growth and toward social justice and quality of life for their citizens.

The time has come for these broader metrics to become more than just descriptive, Laurent argues; applied carefully by private and public decision makers, they can foster genuine progress. He begins by taking stock of the booming field of well-being and sustainability indicators, and explains the insights that the best of these can offer. He then shows how these indicators can be used to develop new policies, from the local to the global….(More)”.

Understanding Corporate Data Sharing Decisions: Practices, Challenges, and Opportunities for Sharing Corporate Data with Researchers

Curated on Posted on by Stefaan Verhulst

Leslie Harris at the Future of Privacy Forum: “Data has become the currency of the modern economy. A recent study projects the global volume of data to grow from about 0.8 zettabytes (ZB) in 2009 to more than 35 ZB in 2020, most of it generated within the last two years and held by the corporate sector.

As the cost of data collection and storage becomes cheaper and computing power increases, so does the value of data to the corporate bottom line. Powerful data science techniques, including machine learning and deep learning, make it possible to search, extract and analyze enormous sets of data from many sources in order to uncover novel insights and engage in predictive analysis. Breakthrough computational techniques allow complex analysis of encrypted data, making it possible for researchers to protect individual privacy, while extracting valuable insights.

At the same time, these newfound data sources hold significant promise for advancing scholarship and shaping more impactful social policies, supporting evidence-based policymaking and more robust government statistics, and shaping more impactful social interventions. But because most of this data is held by the private sector, it is rarely available for these purposes, posing what many have argued is a serious impediment to scientific progress.

A variety of reasons have been posited for the reluctance of the corporate sector to share data for academic research. Some have suggested that the private sector doesn’t realize the value of their data for broader social and scientific advancement. Others suggest that companies have no “chief mission” or public obligation to share. But most observers describe the challenge as complex and multifaceted. Companies face a variety of commercial, legal, ethical, and reputational risks that serve as disincentives to sharing data for academic research, with privacy – particularly the risk of reidentification – an intractable concern. For companies, striking the right balance between the commercial and societal value of their data, the privacy interests of their customers, and the interests of academics presents a formidable dilemma.

To be sure, there is evidence that some companies are beginning to share for academic research. For example, a number of pharmaceutical companies are now sharing clinical trial data with researchers, and a number of individual companies have taken steps to make data available as well. What is more, companies are also increasingly providing open or shared data for other important “public good” activities, including international development, humanitarian assistance and better public decision-making. Some are contributing to data collaboratives that pool data from different sources to address societal concerns. Yet, it is still not clear whether and to what extent this “new era of data openness” will accelerate data sharing for academic research.

Today, the Future of Privacy Forum released a new study, Understanding Corporate Data Sharing Decisions: Practices, Challenges, and Opportunities for Sharing Corporate Data with Researchers. In this report, we aim to contribute to the literature by seeking the “ground truth” from the corporate sector about the challenges they encounter when they consider making data available for academic research. We hope that the impressions and insights gained from this first look at the issue will help formulate further research questions, inform the dialogue between key stakeholders, and identify constructive next steps and areas for further action and investment….(More)”.

Smart Cities, Smarter Citizens

Curated on Posted on by Stefaan Verhulst

Free eBook courtesy of PTC.com: “The smart city movement is on a roll. Technology leaders are looking to transform major cities through advanced computer technologies, sensors, high-speed data networks, predictive analytics, big data, and IoT. But, as Mike Barlow explains in this O’Reilly report, the story goes beyond technology. Citizens, too, will need to play a large role in turning cities into smart, livable environments.

According to a United Nations report, by 2050 two-thirds of humanity will live in more than 40 mega-cities of 10 million people each. All of them will need to determine how to deliver more services with fewer resources. Cities will have to improve efficiency and reduce expenditures wherever possible, through new technologies and other means.

To create a thriving environment where innovation can blossom, citizens will not only be called upon to generate much of the data, but they’ll also need to be at the center of decision-making, based on what that data reveals.

Download this report today, and learn about the progress that various groups and organizations have already made in major cities around the world, and what lies ahead for all of us….(More)”.

Open government and citizen participation: an empirical analysis of citizen expectancy towards open government data

Curated on Posted on by Stefaan Verhulst

Bernd W. Wirtz, Jan C. Weyerer, and Michael Rösch in the International Review of Administrative Sciences: “Citizens are at the heart of open government, and their participation represents a fundamental principle of the latter. Despite their essential role and the great potential benefits open government holds for the public, challenges of use among citizens persist. Previous empirical research has scarcely addressed these issues from a citizen perspective. This study investigates the determinants of open government data use by citizens in Germany. Our results indicate that ease of use, usefulness, as well as transparency, participation and collaboration expectancies significantly determine citizens’ intention to use open government data, which in turn positively affects their word-of-mouth intention. Overall, the findings not only contribute to our understanding of citizen behavior in the context of open government research, especially shedding light on the key aspects of citizens’ usage intention, but also provide implications for both researchers and practitioners.

Points for practitioners

Citizen-based use of open government data (OGD) has multiple facets that practitioners should be aware of. Public administration needs to take account of the important role of accessibility and usability in providing OGD services, with the objective of meeting the major challenge of enabling equal access for all populations via appropriate channels and customization. The content-related preparation of OGD services should seek to enhance transparency, participation and collaboration, raising and shaping respective expectations among citizens. Finally, practitioners should pay particular attention to the opportunities and risks associated with word-of-mouth communication in the context of OGD….(More)”

Participatory Budgeting: Does Evidence Match Enthusiasm?

Curated on Posted on by Stefaan Verhulst

Brian Wampler, Stephanie McNulty, and Michael Touchton at Open Government Partnership: “Participatory budgeting (PB) empowers citizens to allocate portions of public budgets in a way that best fits the needs of the people. In turn, proponents expect PB to improve citizens’ lives in important ways, by expanding their participation in politics, providing better public services such as in healthcare, sanitation, or education, and giving them a sense of efficacy.

Below we outline several potential outcomes that emerge from PB. Of course, assessing PB’s potential impact is difficult, because reliable data is rare and PB is often one of several programs that could generate similar improvements at the same time. Impact evaluations for PB are thus at a very early stage. Nevertheless, considerable case study evidence and some broader, comparative studies point to outcomes in the following areas:

Citizens’ attitudes: Early research focused on the attitudes of citizens who participate in PB, and found that PB participants feel empowered, support democracy, view the government as more effective, and better understand budget and government processes after participating (Wampler and Avritzer 2004; Baiocchi 2005; Wampler 2007).

Participants’ behavior: Case-study evidence shows that PB participants increase their political participation beyond PB and join civil society groups. Many scholars also expect PB to strengthen civil society by increasing its density (number of groups), expanding its range of activities, and brokering new partnerships with government and other CSOs. There is some case study evidence that this occurs (Baiocchi 2005; McNulty 2011; Baiocchi, Heller and Silva 2011; Van Cott 2008) as well as evidence from over 100 PB programs across Brazil’s larger municipalities (Touchton and Wampler 2014). Proponents also expect PB to educate government officials surrounding community needs, to increase their support for participatory processes, and to potentially expand participatory processes in complementary areas. Early reports from five counties in Kenya suggest that PB ther is producing at least some of these impacts.

Electoral politics and governance: PB can also promote social change, which may alter local political calculations and the ways that governments operate. PB may deliver votes to the elected officials that sponsor it, improve budget transparency and resource allocation, decrease waste and fraud, and generally improve accountability. However, there is very little evidence in this area because few studies have been able to measure these impacts in any direct way.

Social well-being: Finally, PB is designed to improve residents’ well-being. Implemented PB projects include funding for healthcare centers, sewage lines, schools, wells, and other areas that contribute directly to well-being. These effects may take years to appear, but recent studies attribute improvements in infant mortality in Brazil to PB (Touchton and Wampler 2014; Gonçalves 2014). Beyond infant mortality, the range of potential impacts extends to other health areas, sanitation, education, and poverty in general. We are cautious here because results from Brazil might not appear elsewhere: what works in urban Brazil might not in rural Indonesia….(More)”.

Bot.Me: A revolutionary partnership

Curated on Posted on by Stefaan Verhulst

PWC Consumer Intelligence Series: “The modern world has been shaped by the technological revolutions of the past, like the Industrial Revolution and the Information Revolution. The former redefined the way the world values both human and material resources; the latter redefined value in terms of resources while democratizing information. Today, as technology progresses even further, value is certain to shift again, with a focus on sentiments more intrinsic to the human experience: thinking, creativity, and problem-solving. AI, shorthand for artificial intelligence, defines technologies emerging today that can understand, learn, and then act based on that information. Forms of AI in use today include digital assistants, chatbots, and machine learning.

Today, AI works in three ways:

  • Assisted intelligence, widely available today, improves what people and organizations are already doing. A simple example, prevalent in cars today, is the GPS navigation program that offers directions to drivers and adjusts to road conditions.
  • Augmented intelligence, emerging today, enables people and organizations to do things they couldn’t otherwise do. For example, the combination of programs that organize cars in ride-sharing services enables businesses that could not otherwise exist.
  • Autonomous intelligence, being developed for the future, establishes machines that act on their own. An example of this will be self-driving vehicles, when they come into widespread use.

With a market projected to reach $70 billion by 2020, AI is poised to have a transformative effect on consumer, enterprise, and government markets around the world. While there are certainly obstacles to overcome, consumers believe that AI has the potential to assist in medical breakthroughs, democratize costly services, elevate poor customer service, and even free up an overburdened workforce. Some tech optimists believe AI could create a world where human abilities are amplified as machines help mankind process, analyze, and evaluate the abundance of data that creates today’s world, allowing humans to spend more time engaged in high-level thinking, creativity, and decision-making. Technological revolutions, like the Industrial Revolution and the Information Revolution, didn’t happen overnight. In fact, people in the midst of those revolutions often didn’t even realize they were happening, until history was recorded later.

That is where we find ourselves today, in the very beginning of what some are calling the “augmented age.” Just like humans in the past, it is up to mankind to find the best ways to leverage these machine revolutions to help the world evolve. As Isaac Asimov, the prolific science fiction writer with many works on AI mused, “No sensible decision can be made any longer without taking into account not only the world as it is, but the world as it will be.” As a future with AI approaches, it’s important to understand how people think of it today, how it will amplify the world tomorrow, and what guiding principles will be required to navigate this monumental change….(More)”.

Augmented CI and Human-Driven AI: How the Intersection of Artificial Intelligence and Collective Intelligence Could Enhance Their Impact on Society

Curated on Posted on by Stefaan Verhulst

Blog by Stefaan Verhulst: “As the technology, research and policy communities continue to seek new ways to improve governance and solve public problems, two new types of assets are occupying increasing importance: data and people. Leveraging data and people’s expertise in new ways offers a path forward for smarter decisions, more innovative policymaking, and more accountability in governance. Yet, unlocking the value of these two assets not only requires increased availability and accessibility (through, for instance, open data or open innovation), it also requires innovation in methodology and technology.

The first of these innovations involves Artificial Intelligence (AI). AI offers unprecedented abilities to quickly process vast quantities of data that can provide data-driven insights to address public needs. This is the role it has for example played in New York City, where FireCast, leverages data from across the city government to help the Fire Department identify buildings with the highest fire risks. AI is also considered to improve education, urban transportation,  humanitarian aid and combat corruption, among other sectors and challenges.

The second area is Collective Intelligence (CI). Although it receives less attention than AI, CI offers similar potential breakthroughs in changing how we govern, primarily by creating a means for tapping into the “wisdom of the crowd” and allowing groups to create better solutions than even the smartest experts working in isolation could ever hope to achieve. For example, in several countries patients’ groups are coming together to create new knowledge and health treatments based on their experiences and accumulated expertise. Similarly, scientists are engaging citizens in new ways to tap into their expertise or skills, generating citizen science – ranging from mapping our solar system to manipulating enzyme models in a game-like fashion.

Neither AI nor CI offer panaceas for all our ills; they each pose certain challenges, and even risks.  The effectiveness and accuracy of AI relies substantially on the quality of the underlying data as well as the human-designed algorithms used to analyse that data. Among other challenges, it is becoming increasingly clear how biases against minorities and other vulnerable populations can be built into these algorithms. For instance, some AI-driven platforms for predicting criminal recidivism significantly over-estimate the likelihood that black defendants will commit additional crimes in comparison to white counterparts. (for more examples, see our reading list on algorithmic scrutiny).

In theory, CI avoids some of the risks of bias and exclusion because it is specifically designed to bring more voices into a conversation. But ensuring that that multiplicity of voices adds value, not just noise, can be an operational and ethical challenge. As it stands, identifying the signal in the noise in CI initiatives can be time-consuming and resource intensive, especially for smaller organizations or groups lacking resources or technical skills.

Despite these challenges, however, there exists a significant degree of optimism  surrounding both these new approaches to problem solving. Some of this is hype, but some of it is merited—CI and AI do offer very real potential, and the task facing both policymakers, practitioners and researchers is to find ways of harnessing that potential in a way that maximizes benefits while limiting possible harms.

In what follows, I argue that the solution to the challenge described above may involve a greater interaction between AI and CI. These two areas of innovation have largely evolved and been researched separately until now. However, I believe that there is substantial scope for integration, and mutual reinforcement. It is when harnessed together, as complementary methods and approaches, that AI and CI can bring the full weight of technological progress and modern data analytics to bear on our most complex, pressing problems.

To deconstruct that statement, I propose three premises (and subsequent set of research questions) toward establishing a necessary research agenda on the intersection of AI and CI that can build more inclusive and effective approaches to governance innovation.

Premise I: Toward Augmented Collective Intelligence: AI will enable CI to scale

Premise II: Toward Human-Driven Artificial Intelligence: CI will humanize AI

Premise III: Open Governance will drive a blurring between AI and CI

…(More)”.

Once Upon an Algorithm: How Stories Explain Computing

Curated on Posted on by Stefaan Verhulst

Book by Martin Erwig: “Picture a computer scientist, staring at a screen and clicking away frantically on a keyboard, hacking into a system, or perhaps developing an app. Now delete that picture. In Once Upon an Algorithm, Martin Erwig explains computation as something that takes place beyond electronic computers, and computer science as the study of systematic problem solving. Erwig points out that many daily activities involve problem solving. Getting up in the morning, for example: You get up, take a shower, get dressed, eat breakfast. This simple daily routine solves a recurring problem through a series of well-defined steps. In computer science, such a routine is called an algorithm.

Erwig illustrates a series of concepts in computing with examples from daily life and familiar stories. Hansel and Gretel, for example, execute an algorithm to get home from the forest. The movie Groundhog Day illustrates the problem of unsolvability; Sherlock Holmes manipulates data structures when solving a crime; the magic in Harry Potter’s world is understood through types and abstraction; and Indiana Jones demonstrates the complexity of searching. Along the way, Erwig also discusses representations and different ways to organize data; “intractable” problems; language, syntax, and ambiguity; control structures, loops, and the halting problem; different forms of recursion; and rules for finding errors in algorithms.

This engaging book explains computation accessibly and shows its relevance to daily life. Something to think about next time we execute the algorithm of getting up in the morning…(More)”.

Most of the public doesn’t know what open data is or how to use it

Curated on Posted on by Stefaan Verhulst

Jason Shueh at Statescoop: “New survey results show that despite the aggressive growth of open data, there is a drastic need for greater awareness and accessibility.

Results of a global survey published last month by Singapore’s Government Technology agency (GovTech) and the Economist Intelligence Unit, a British forecasting and advisory firm, show that open data is not being utilized as effectively as it could be. Researchers surveyed more than 1,000 residents in the U.S. and nine other leading open data counties and found that “an overwhelming” number of respondents say the primary barrier to open data’s use and effectiveness is a lack of public awareness.

The study reports that 50 percent of respondents said that national and local governments need to expand their civic engagements efforts on open data.

“Half of respondents say there is not enough awareness in their country about open government data initiatives and their benefits or potential uses,” the reports notes. “This is seen as the biggest barrier to more open government data use, particularly by citizens in India and Mexico.”

Accessibility is named as the second largest hurdle, with 31 percent calling for more relevant data. Twenty-five percent say open data is difficult to use due to a lack of standardized formats and another 25 percent say they don’t have the skills to understand open data.

Those calling for more relevant data say they wanted to see more information on crime, the economy and the environment, yet report they are happy with the availability and use of open data related to transportation….

When asked to name the main benefit of open data, 70 percent say greater transparency, 78 percent say to drive a better quality of life, and 53 percent cite better decision making….(More)”.

Crowdsourced Smart Cities

Curated on Posted on by Stefaan Verhulst

Paper by Robert A Iannucci and Anthony Rowe: “The vision of applying computing and communication technologies to enhance life in our cities is fundamentally appealing. Pervasive sensing and computing can alert us to imminent dangers, particularly with respect to the movement of vehicles and pedestrians in and around crowded streets. Signaling systems can integrate knowledge of city-scale traffic congestion. Self-driving vehicles can borrow from and contribute to a city-scale information collaborative. Achieving this vision will require significant coordination among the creators of sensors, actuators, and application-level software systems. Cities will invest in such smart infrastructure if and only if they are convinced that the value can be realized. Investment by technology providers in creation of the infrastructure depends to a large degree on their belief in a broad and ready market. To accelerate innovation, this stalemate must be broken. Borrowing a page from the evolution of the internet, we put forward the notion that an initially minimalist networking infrastructure that is well suited to smart city concepts can break this cycle and empower co-development of both clever city-sensing devices and valuable city-scale applications, with players large and small being empowered in the process. We call this the crowdsourced smart city concept. We illustrate the concept via an examination of our ongoing project to crowdsource real-time traffic data, arguing that this can rapidly generalize to many more smart city applications. This exploration motivates study of a number of smart city challenges, crowdsourced or otherwise, leading to a paradigm shift we call edgeless computing….(More)”.